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license: mit
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# ResNetWildFireModel for Wildfire Classification

## Model Details

- **Model Architecture:** ResNet-18 (Modified)  
- **Framework:** PyTorch  
- **Input Shape:** 3-channel RGB images  
- **Number of Parameters:** ~11.7M (Based on ResNet-18)  
- **Output:** Binary classification (wildfire presence)

## Model Description

This model is a **fine-tuned ResNet-18** for wildfire classification. The pretrained **ResNet-18** backbone is used with its feature extractor **frozen**, while only the **final fully connected layer** is trained. The last fully connected layer has been replaced with a **single output neuron** for binary classification, predicting the presence of wildfire.

## Training Details

- **Optimizer:** Adam  
- **Batch Size:** 32  
- **Loss Function:** Binary Cross-Entropy (BCE)  
- **Number of Epochs:** 10  
- **Dataset:** [Wildfire Detection Image Data](https://www.kaggle.com/datasets/brsdincer/wildfire-detection-image-data)  

### Losses Per Epoch

| Epoch | Training Loss | Validation Loss |
|-------|---------------|-----------------|
| 1     | 0.2182        | 0.0593          |
| 2     | 0.0483        | 0.0508          |
| 3     | 0.0347        | 0.0482          |
| 4     | 0.0275        | 0.0461          |
| 5     | 0.0253        | 0.0474          |
| 6     | 0.0187        | 0.0457          |
| 7     | 0.0131        | 0.0456          |
| 8     | 0.0111        | 0.0451          |
| 9     | 0.0096        | 0.0463          |
| 10    | 0.0079        | 0.0474          |

## License

This model is released under the **MIT License**.

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